Curve Fitting to identify rise or fall of signal

inverseSigmoid.py

# Code to fit the inverse sigmoid curve to tail end of signal
def sigmoid(x, L ,x0, k, b):
  y = L / (1 + np.exp(k*(x-x0)))+b
  return (y)

def isCurveSigmoid(pixelCounts, count):
  try:
    xIndex = len(pixelCounts)
    p0 = [max(pixelCounts), np.median(xIndex),1,min(pixelCounts)] # this is an mandatory initial guess
    popt, pcov = curve_fit(sigmoid, list(range(xIndex)), pixelCounts, p0, method='lm', maxfev=5000)
    yVals = sigmoid(list(range(xIndex)), *popt)
    # May have to check for a value much less than Median to avoid false positives.
    if np.median(yVals[:10]) - np.median(yVals[-10:]) > 15:
    print('Event Triggered...')
    return True
  except Exception as err:
    print(traceback.format_exc())
  return False

def findCurveFit(eye, image, pixelCount, frame_count, numFrames = 50):
  triggerEvent = False
  
  if (len(image) == 0):
    return pixelCount, False
    
  # Convert to gray scale as histogram works well on 256 values.
  gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
  
  # calculate frequency of pixels in range 0-255
  histg = cv2.calcHist([gray],[0],None,[256],[0,256])
  
  # hack to know whether eye is closed or not.
  # more spread of pixels in a histogram signifies an opened eye
  activePixels = np.count_nonzero(histg)
  pixelCount.append(activePixels)
  if len(pixelCount) > numFrames and frame_count % 15 == 0:
    if isCurveSigmoid(pixelCount[-numFrames+10:], len(pixelCount)):
      print('Event Triggered...')
      pixelCount.clear()
      plt.clf()
      triggerEvent = True
  
  return pixelCount, triggerEvent